Attention induction for a CT volume classification of COVID-19

1Citations
Citations of this article
8Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Purpose: This study proposes a method to draw attention toward the specific radiological findings of coronavirus disease 2019 (COVID-19) in CT images, such as bilaterality of ground glass opacity (GGO) and/or consolidation, in order to improve the classification accuracy of input CT images. Methods: We propose an induction mask that combines a similarity and a bilateral mask. A similarity mask guides attention to regions with similar appearances, and a bilateral mask induces attention to the opposite side of the lung to capture bilaterally distributed lesions. An induction mask for pleural effusion is also proposed in this study. ResNet18 with nonlocal blocks was trained by minimizing the loss function defined by the induction mask. Results: The four-class classification accuracy of the CT images of 1504 cases was 0.6443, where class 1 was the typical appearance of COVID-19 pneumonia, class 2 was the indeterminate appearance of COVID-19 pneumonia, class 3 was the atypical appearance of COVID-19 pneumonia, and class 4 was negative for pneumonia. The four classes were divided into two subgroups. The accuracy of COVID-19 and pneumonia classifications was evaluated, which were 0.8205 and 0.8604, respectively. The accuracy of the four-class and COVID-19 classifications improved when attention was paid to pleural effusion. Conclusion: The proposed attention induction method was effective for the classification of CT images of COVID-19 patients. Improvement of the classification accuracy of class 3 by focusing on features specific to the class remains a topic for future work.

References Powered by Scopus

Deep residual learning for image recognition

174329Citations
N/AReaders
Get full text

ImageNet Large Scale Visual Recognition Challenge

30428Citations
N/AReaders
Get full text

Delving deep into rectifiers: Surpassing human-level performance on imagenet classification

15471Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Attention induction based on pathologist annotations for improving whole slide pathology image classifier

0Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Takateyama, Y., Haruishi, T., Hashimoto, M., Otake, Y., Akashi, T., & Shimizu, A. (2023). Attention induction for a CT volume classification of COVID-19. International Journal of Computer Assisted Radiology and Surgery, 18(2), 289–301. https://doi.org/10.1007/s11548-022-02769-y

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 1

100%

Readers' Discipline

Tooltip

Medicine and Dentistry 1

100%

Save time finding and organizing research with Mendeley

Sign up for free